1. Field of the Invention
The present invention relates to a technique of predicting a color signal of an output color space from a color signal of an input color space or predicting a color signal of an input color space from a color signal of an output color space in a color output apparatusesuch as a printer in which color signals of a color space such as CMYK are employed as input color signals and color signals of a color space such as L*a*b* are employed as output color signals; in a color output apparatusesuch as a display in which color signals of a color space such as RGB are used as input color signals and color signals of a color space such as XYZ are used as output color signals; or in a color input apparatusesuch as a scanner in which color signals of a color space such as L*a*b* are employed as input color signals and color signals of a color space such as RGB are employed as output color signals. More specifically, the present invention relates to a technique for calculating data accuracy of a real data pair between an input color signal and an output color signal used in a case where a model functioning as a prediction base is formed.
2. Description of the Related Art
Conventionally, various methods for predicting characteristics of a color image output apparatus such as printers and displays, and also for predicting characteristics of color image input apparatuses such as scanners have been tried. In general, a prediction of the color transfer characteristic for each of color image input/output apparatuses is carried out using a real data pair for each apparatus. For instance, in a printer (will be referred to as “CMYK printer” hereinafter) for forming an image using color materials having four colors of C (cyan), M (magenta), Y (yellow), and K (black), a plurality of color patches represented by CMYK are printed out. Then, the color patches are calorimetrically measured to obtain device-independent L*a*b* calorimetric values. Then, while the CMYK values corresponding to the color patches and the measured L*a*b* calorimetric values are defined as a real data pair, colors which don't appear in the color patches can be predicted based upon the real data pair.
JP-A-10-262157 discloses a method using the regression analysis as a predicting method of the color transfer characteristic, making it possible to predict the color in high accuracy using the real data pair. The predicting method is not limited to this method, but various predicting methods have been proposed, for example, predicting methods using a neural network.
However, even though color transfer characteristics can be predicted in high accuracy, when an abnormal value is contained in the real data pair, a high-accuracy color transfer characteristic cannot be predicted. Also, when a colorimetric-measurement sequence of color patches is erroneously performed during a step of forming real data pairs, although colorimetric values themselves aren't abnormal, a real data pair obtained in the erroneous colorimetric-measuring sequence may be eventually regarded as an abnormal value. As to prediction of a color transfer characteristic, the higher the accuracy of the prediction model becomes, the more the predicted value is fitted to the abnormal value, so that desirable colors cannot be obtained.
To solve such a problem, JP-A-10-262157 discloses a method of quantifying the accuracy of the real data pairs. The data accuracy calculating method disclosed in JP-A-10-262157 tests the real data pairs one by one using a color transfer characteristic prediction model disclosed therein. Precisely speaking, the accuracy of the data is tested based on a direction of the predicted value and a direction of the real data in the case that the real data is predicted using the real data itself. The accuracy is calculated in such a manner that if the direction of the predicted value and the direction of the real data are the same directions, the large weight is applied to the predicted value, whereas if these directions of the predicted value and the read data are opposed to each other, the small weight is applied to the predicted value.
However, the above-described data accuracy calculating method largely depends on the prediction model disclosed in JP-A-10-262157. It also largely depends on parameters of the prediction model whether or not the accuracy of the real data can be calculated correctly. If the parameter of the prediction model used in calculating the accuracy is properly selected, unstable conditions of prediction can be solved. However, since only the directivity of the real data and the directivity of the predicted value are considered, the fluctuations contained in the data cannot be correctly quantified when the abnormal value are mixed with the predicted value. As a result, the abnormal value cannot be given a proper accuracy.
In addition to JP-A-10-262157, there is another method for testing the abnormal value using a statistical method disclosed in JP-A-7-219929. This statistical method disclosed in JP-A-219929 calculates a statistic value with respect to one dimensional data distribution to test the abnormal value. However, this statistical method cannot endure a calculation of accuracy of real data in which an input signal and an output signal form a pair in a distribution such as a color processing capable of handling multi-dimension.
As described above, generally speaking, as to not only the color processing, but also the method for testing the abnormal value, many methods handles the distribution of one dimension data. It is not easy to check the abnormal value with respect to the real data pair used in the color processing, but also not easy to calculate the accuracy with respect to all of the real data pairs included in the real data set. In the color processing, while the input/output relationship has multi-dimensional relationship, the output side corresponds to the multi-dimensional calorimetric value vector.